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--- |
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language: |
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- en |
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- id |
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tags: |
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- poultry |
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- chicken |
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- animal-health |
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- vocalization-analysis |
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- early-disease-detection |
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- sound-classification |
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- pytorch |
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- Indonesia |
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datasets: |
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- IceKhoffi/chicken-health-behavior-multimodal |
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--- |
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# `Chicken Vocalization Classifier` |
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This model is designed for classifying chicken vocalizations into categories indicative of health status or environmental noise. It serves as a crucial audio-based component within the "Chicken Health & Behavior Detection" multimodal project, aiming to aid in the early detection of poultry diseases and the monitoring of farm conditions. |
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## Model Description |
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The `chicken-vocalization-classifier` is a Convolutional Neural Network (CNN) built with PyTorch, designed to process Log-Mel Spectrogram representations of audio recordings. It categorizes chicken sounds into three classes: `Healthy`, `Noise`, and `Unhealthy`. This model can help identify abnormal vocalizations (e.g., coughing, distress calls) that might signal health issues, or distinguish between relevant chicken sounds and general farm noise. |
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## Training Data |
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This model was trained using the "Poultry Vocalization Signal Dataset for Early Disease Detection". |
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## Training Procedure |
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The model was implemented and trained using the PyTorch framework |
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* **Model Architecture:** The model, named `ModdifiedModel`, consists of a `features` extractor and a `classifier` head. |
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* **Features Extractor (Sequential):** Composed of three blocks, each containing a `Conv2d` layer, `BatchNorm2d`, `ReLU` activation, and `MaxPool2d`. |
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* Block 1: `Conv2d(1, 32, kernel_size=3)`, `BatchNorm2d(32)`, `ReLU()`, `MaxPool2d(2)` |
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* Block 2: `Conv2d(32, 64, kernel_size=3)`, `BatchNorm2d(64)`, `ReLU()`, `MaxPool2d(2)` |
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* Block 3: `Conv2d(64, 128, kernel_size=3)`, `BatchNorm2d(128)`, `ReLU()`, `MaxPool2d(2)` |
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* **Classifier (Sequential):** Contains a `Flatten` layer, two `Linear` layers, `Dropout`, and `ReLU` activation. |
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* `Linear(in_features=25088, out_features=256)` |
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* `Dropout(0.5)` |
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* `ReLU()` |
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* `Linear(in_features=256, out_features=3)` (for 3 classes) |
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* **Preprocessing:** Audio files are converted to Log-Mel Spectrograms using `librosa`. |
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* `SAMPLE_RATE = 22050` Hz |
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* Audio is sampled to approximately 1.5 seconds (`WAV_SIZE = int(1.5 * SAMPLE_RATE)`) |
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* `MEL_BANDS = 128` |
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* `N_FFT = 2648` |
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* `HOP_LENGTH = 256` |
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* **Data Splitting:** The dataset was split into training and testing sets using `train_test_split` with `test_size=0.2` and `random_state=27` |
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* **Loss Function:** `nn.CrossEntropyLoss()` |
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* **Optimizer:** `torch.optim.Adam` with a learning rate (`lr`) of `0.001` |
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* **Epochs:** The model was trained for `30` epochs |
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* **Batch Size:** Training was performed with a `batch_size` of `32` |
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## Performance |
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The Modified model was evaluated on a test set. |
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## How to Use |
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You can load this trained model's weights with PyTorch. For full usage examples, including audio preprocessing steps and inference, please refer to the `CHBD_Vocalization_Analysis.ipynb` notebook provided in this repository. |
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```python |
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from huggingface_hub import hf_hub_download |
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import torch |
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import torch.nn as nn |
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import os |
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# Define the ModdifiedModel class |
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# (You will need to copy this class definition from the CHBD_Vocalization_Analysis.ipynb file) |
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class ModdifiedModel(nn.Module): |
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def __init__(self, num_classes=3): |
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super(ModdifiedModel, self).__init__() |
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# ... (copy the full model architecture definition here) ... |
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def forward(self, x): |
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# ... (copy the forward pass definition here) ... |
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# Instantiate the model |
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model = ModdifiedModel(num_classes=3) |
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# Define each Hugging Face details |
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repo_id = "IceKhoffi/chicken-vocalization-classifier" |
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filename = "Chiken_CNN_Disease_Detection_Model.pth" |
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model_path = hf_hub_download(repo_id=repo_id, filename=filename) |
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state_dict = torch.load(model_path, map_location='cpu') |
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model.load_state_dict(state_dict) |
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# Set model to evaluation mode |
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model.eval() |
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# The model is now loaded and ready for inference. |
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# Refer to the provided .ipynb for detailed preprocessing and inference examples. |
